Qaraqe Marwa, Ismail Muhammad, Serpedin Erchin
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.
Epilepsy Behav. 2015 Sep;50:77-87. doi: 10.1016/j.yebeh.2015.06.002. Epub 2015 Jul 3.
This paper presents two novel epileptic seizure onset detectors. The detectors rely on a common spatial pattern (CSP)-based feature enhancement stage that increases the variance between seizure and nonseizure scalp electroencephalography (EEG). The proposed feature enhancement stage enables better discrimination between seizure and nonseizure features. The first detector adopts a conventional classification stage using a support vector machine (SVM) that feeds the energy features extracted from different subbands to an SVM for seizure onset detection. The second detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results have demonstrated that the first detector achieves a sensitivity of 95.2%, detection latency of 6.43s, and false alarm rate of 0.59perhour. The second detector achieves a sensitivity of 100%, detection latency of 7.28s, and false alarm rate of 1.2per hour for the MAJORITY fusion method.
本文提出了两种新型癫痫发作起始检测器。这些检测器依赖于一个基于共同空间模式（CSP）的特征增强阶段，该阶段可增加癫痫发作和非癫痫发作头皮脑电图（EEG）之间的差异。所提出的特征增强阶段能够更好地区分癫痫发作和非癫痫发作特征。第一种检测器采用传统的分类阶段，使用支持向量机（SVM），将从不同子带提取的能量特征输入到SVM中进行癫痫发作起始检测。第二种检测器使用逻辑运算符对在不同EEG频谱带独立进行的SVM癫痫发作起始检测结果进行汇总。与现有检测器相比，所提出的检测器在灵敏度和检测延迟方面表现出更好的性能。实验结果表明，第一种检测器的灵敏度为95.2%，检测延迟为6.43秒，误报率为每小时0.59次。对于多数融合方法，第二种检测器的灵敏度为100%，检测延迟为7.28秒，误报率为每小时1.2次。
